US7613342B1 - Method and device for displaying or searching for object in image and computer-readable storage medium - Google Patents

Method and device for displaying or searching for object in image and computer-readable storage medium Download PDF

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US7613342B1
US7613342B1 US09/786,352 US78635200A US7613342B1 US 7613342 B1 US7613342 B1 US 7613342B1 US 78635200 A US78635200 A US 78635200A US 7613342 B1 US7613342 B1 US 7613342B1
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Prior art keywords
outline
image
additional parameter
shape descriptor
shape
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Miroslaw Z. BOBER
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Rakuten Group Inc
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Mitsubishi Electric Corp
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
    • G06F16/5854—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using shape and object relationship
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
    • G06V10/752—Contour matching
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S707/00—Data processing: database and file management or data structures
    • Y10S707/99931—Database or file accessing
    • Y10S707/99933—Query processing, i.e. searching
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S707/00—Data processing: database and file management or data structures
    • Y10S707/99931—Database or file accessing
    • Y10S707/99933—Query processing, i.e. searching
    • Y10S707/99936—Pattern matching access

Definitions

  • the present invention relates to the representation of an object appearing in a still or video image, such as an image stored in a multimedia database, especially for searching purposes, and to a method and apparatus for searching for an object using such a representation.
  • CSS Curvature Scale Space
  • the CSS representation uses a curvature function for the outline of the object, starting from an arbitrary point on the outline.
  • the curvature function is studied as the outline shape is evolved by a series of deformations which smooth the shape. More specifically, the zero crossings of the derivative of the curvature function convolved with a family of Gaussian filters are computed. The zero crossings are plotted on a graph, known as the Curvature Scale Space, where the x-axis is the normalised arc-length of the curve and the y-axis is the evolution parameter, specifically, the parameter of the filter applied.
  • the plots on the graph form loops characteristic of the outline.
  • Each convex or concave part of the object outline corresponds to a loop in the CSS image.
  • the co-ordinates of the peaks of the most prominent loops in the CSS image are used as a representation of the outline.
  • the CSS representation of an input shape is calculated.
  • the similarity between an input shape and stored shapes is determined by comparing the position and height of the peaks in the respective CSS images using a matching algorithm.
  • a problem with the representation as described above is that retrieval accuracy is sometimes poor, especially for curves which have a small number of concavities or convexities. In particular, the representation cannot distinguish between various convex curves.
  • An aspect of the present invention is to introduce an additional means of describing the shape of the “prototype contour shape”.
  • the prototype contour shape is defined here preferably as:
  • the shape of the prototype contour can be described by means of the invariants based on region moments as described in the paper “Visual Pattern Recognition by Moments Invariants”, IEEE Transaction on Information Theory, Vol. IT-8, 179-187, 1962 by M. K. Hu the contents of which are incorporated herein by reference or using the Fourier descriptors as described in the paper “On Image Analysis by the Methods of Moments”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 10, No. 4, July 1988, by Cho-Huak The, the contents of which are incorporated herein by reference, or parameters such as eccentricity, circularity, etc. In the known method mentioned above, eccentricity and circularity is only used in relation to the original shape.
  • the retrieval accuracy can be improved.
  • the method comprises deriving a curvature scale space (CSS) representation of the object outline by smoothing the object outline, deriving at least one additional parameter reflecting the shape or mass distribution of a smoothed version of the original curve, and associating the CSS representation and the additional parameter as a shape descriptor of the object.
  • CSS curvature scale space
  • an additional parameter relates to the smoothed outline corresponding to a peak in the CSS image.
  • an additional parameter relates to the smoothed outline corresponding to the highest peak in the CSS image.
  • additional parameter corresponds to the eccentricity of the outline.
  • an additional parameter corresponds to the circularity of the outline.
  • At least one additional parameter uses a region-based representation.
  • an additional parameter is a region moment invariant.
  • an additional parameter is based on Fourier descriptors.
  • an additional parameter is based on Zernike moments of the region enclosed by the outline.
  • the method comprises, for each object outline, determining if there are significant changes in curvature in the object outline, and, if there are significant changes in curvature of the object outline, then deriving a shape descriptor using a method as described herein and, if there are no significant changes in curvature of the object outline, then deriving a shape descriptor including at least said additional parameter reflecting the shape of the object outline.
  • the additional parameter for an object outline having no significant changes in curvature is based on region moment invariants, Fourier descriptors or Zernike moments of the outline.
  • a method of searching for an object in a still or video image by processing signals corresponding to images comprises inputting a query in the form of a two-dimensional outline, deriving a descriptor of said outline using a method described herein, and comparing said query descriptor with each descriptor for stored objects using a matching procedure using the CSS values and the additional parameters to derive a similarity measure, and selecting and displaying at least one result corresponding to an image containing an object for which the comparison indicates a degree of similarity between the query and said object.
  • a depends on the number and height of the CSS peaks.
  • a method of searching for an object in a still or video image by processing signals corresponding to images comprises calculating a similarity measure between two object outlines using a CSS representation of said outlines and additional parameters reflecting the shape of or mass distribution within the original outline or a smoothed version of the outline.
  • An apparatus described herein is adapted to implement a method as described herein.
  • a computer program described herein implements a method as described herein.
  • a computer system described herein is programmed to operate according to a method as described herein.
  • a computer-readable storage medium described herein stores computer-executable process steps for implementing a method described herein.
  • FIG. 1 is a block diagram of a video database system
  • FIG. 2 is a drawing of an outline of an object
  • FIG. 3 is a CSS representation of the outline of FIG. 2 .
  • FIG. 1 shows a computerised video database system according to an embodiment of the invention.
  • the system includes a control unit 2 in the form of a computer, a display unit 4 in the form of a monitor, a pointing device 6 in the form of a mouse, an image database 8 including stored still and video images and a descriptor database 10 storing descriptors of objects or parts of objects appearing in images stored in the image database 8 .
  • a descriptor for the shape of each object of interest appearing in an image in the image database is derived by the control unit 2 and stored in the descriptor database 10 .
  • the control unit 2 derives the descriptors operating under the control of a suitable program implementing a method as described below.
  • a CSS representation of the outline is derived. This is done using the known method as described in one of the papers mentioned above.
  • the outline is smoothed by convolving ⁇ with an ID Gaussian kernel g(u, ⁇ ), and the curvature zero crossings of the evolving curve are examined as ⁇ changes.
  • the zero crossing are identified using the following expression for the curvature:
  • k ⁇ ( u , ⁇ ) X u ⁇ ( u , ⁇ ) ⁇ Y uu ⁇ ( u , ⁇ ) - X uu ⁇ ( u , ⁇ ) ⁇ Y u ⁇ ( u , ⁇ ) ( X u ⁇ ( u , ⁇ ) 2 + Y u ⁇ ( u , ⁇ ) 2 ) 3 / 2
  • the number of curvature zero crossings changes as ⁇ changes, and when ⁇ is sufficiently high ⁇ is a convex curve with no zero crossings.
  • the zero crossing points (u, ⁇ ) are plotted on a graph, known as the CSS image space.
  • the peaks of the characteristic curves are identified and the corresponding co-ordinates are extracted and stored. In general terms, this gives a set of n co-ordinate pairs [(x1,y1), (x2,y2), . . . (x n ,y n )], where n is the number of peaks, and xi is the arc-length position of the ith peak and yi is the peak height.
  • These peak co-ordinates constitute the CSS representation.
  • the additional parameters are the eccentricity and circularity of the “prototype region” for the shape, where the “prototype region” of the shape is the contour of the shape after the final smoothing step, that is, at the point equivalent to the highest peak value ⁇ .
  • Other values of ⁇ can be selected for the prototype region.
  • the descriptor database 10 of the system of FIG. 1 stores shape descriptors derived according to the method described above.
  • the user initiates a search by drawing an object outline on the display using the pointing device.
  • the control unit 2 then derives a shape descriptor of the input outline in the manner described above.
  • the control unit then performs a matching comparison with each shape descriptor stored in the database.
  • Shape S 1 is being compared with a stored shape S 2 , S 1 and S 2 being respective descriptors:
  • EPR Eccentricity of the prototype region and CPR means Circularity of the prototype region
  • PEAKS means the set of coordinates of peaks in the CSS image (the set can be empty).
  • M a *abs((EPR2 ⁇ EPR1)/(EPR2+EPR1))+ b *abs((CPR2 ⁇ CPR1)/((CPR2+CPR1))+SM(PEAKS1, PEAKS2)
  • SM is the standard similarity measure defined on the two sets of peaks [1], and abs denotes absolute value.
  • SM is calculated using a known matching algorithm such as described in the above-mentioned papers can be used. That matching procedure is briefly described below.
  • the image curve ⁇ i and the model curve ⁇ m and their respective sets of peaks ⁇ (xi1,yi1), (xi2,yi2), . . . , (xin,yin) ⁇ and ⁇ (xm1,ym1), (xm2,ym2), . . . , (xmn,ymn) ⁇ the similarity measure is calculated.
  • the similarity measure is defined as a total cost of matching of peaks in the model into peaks in the image.
  • the matching which minimises the total cost is determined using a dynamic programming.
  • the algorithm recursively matches the peaks from the model to the peaks from the image and calculates the cost of each such match.
  • Each model peak can be matched with only one image peak and each image peak can be matched with only one model peak. Some of the model and or image peak may remain unmatched, and there is an additional penalty cost for each unmatched peak.
  • Two peaks can be matched if their horizontal distance is less than 0.2. The cost of a match is the length of the straight line between the two matched peaks. The cost of an unmatched peak is its height.
  • the image peak (xip,yip) cannot be matched. In that case add its height yip to the total cost and remove only the peak (xip,yip) from the image peak list by marking it as “matched”.
  • the cost of the match as the height of the highest unmatched image or model peak and remove that peak from the list.
  • the above procedure is repeated with the image curve peaks and the model curve peaks swapped.
  • the final matching value is the lower of the two.
  • the similarity measures resulting from the matching comparisons are ordered and the objects corresponding to the descriptors having similarity measures indicating the closest match (i.e. here the lowest similarity measures) are then displayed on the display unit 4 for the user.
  • the number of objects to be displayed can be pre-set or selected by the user.
  • different parameters can be used to describe the shape of the “prototype region”.
  • three Fourier coefficients of the curve can be used.
  • a system according to the invention may, for example, be provided in an image library.
  • the databases may be sited remote from the control unit of the system, connected to the control unit by a temporary link such as a telephone line or by a network such as the internet.
  • the image and descriptor databases may be provided, for example, in permanent storage or on portable data storage media such as CD-ROMs or DVDs.
  • Components of the system as described may be provided in software or hardware form. Although the invention has been described in the form of a computer system, it could be implemented in other forms, for example using a dedicated chip.
  • the invention can also be used, for example, for matching images of objects for verification purposes, or for filtering.

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US09/786,352 1999-07-15 2000-07-12 Method and device for displaying or searching for object in image and computer-readable storage medium Expired - Lifetime US7613342B1 (en)

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GB9916684A GB2352076B (en) 1999-07-15 1999-07-15 Method and apparatus for representing and searching for an object in an image
PCT/JP2000/004673 WO2001006457A1 (fr) 1999-07-15 2000-07-12 Procede et dispositif permettant d'afficher ou de chercher un objet dans une image et support de stockage lisible via ordinateur

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US11/980,482 Expired - Fee Related US7574049B2 (en) 1999-07-15 2007-10-31 Method, apparatus, computer program, computer system and computer-readable storage for representing and searching for an object in an image
US11/980,471 Expired - Fee Related US7646920B2 (en) 1999-07-15 2007-10-31 Method, apparatus, computer program, computer system and computer-readable storage for representing and searching for an object in an image
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Families Citing this family (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2352076B (en) * 1999-07-15 2003-12-17 Mitsubishi Electric Inf Tech Method and apparatus for representing and searching for an object in an image
US6625607B1 (en) * 1999-07-22 2003-09-23 Parametric Technology Corporation Method of comparing parts
GB2384095B (en) * 2001-12-10 2004-04-28 Cybula Ltd Image recognition
JP4477468B2 (ja) * 2004-10-15 2010-06-09 富士通株式会社 組み立て図面の装置部品イメージ検索装置
US8281281B1 (en) * 2006-06-07 2012-10-02 Pixar Setting level of detail transition points
US8031947B2 (en) * 2007-04-03 2011-10-04 Jacobsen Kenneth P Method and system for rapid matching of video streams
CN101334780A (zh) * 2007-06-25 2008-12-31 英特维数位科技股份有限公司 人物影像的搜寻方法、系统及存储影像元数据的记录媒体
KR101033366B1 (ko) * 2009-09-17 2011-05-09 고려대학교 산학협력단 효과적인 3차원 객체 인식을 위한 외형 기반 인덱스 구축 및 검색 방법
KR101032533B1 (ko) * 2009-10-14 2011-05-04 중앙대학교 산학협력단 특징값 정제를 통한 모양 기술 장치 및 방법
KR101767269B1 (ko) * 2011-04-25 2017-08-10 한국전자통신연구원 영상 검색 장치 및 방법
ITTO20120602A1 (it) 2012-07-09 2014-01-10 Sisvel Technology Srl Method for transforming an image descriptor based on a gradient histogram and relative image processing apparatus.
RU2538938C2 (ru) * 2013-04-11 2015-01-10 Федеральное государственное бюджетное образовательное учреждение высшего профессионального образования "Юго-Западный государственный университет" (ЮЗГУ) Способ формирования двумерного изображения биосигнала и его анализа
US10060749B2 (en) * 2015-02-19 2018-08-28 Here Global B.V. Method and apparatus for creating a clothoid road geometry
CN110737796B (zh) * 2019-10-17 2022-06-10 苏州大学 图像检索方法、装置、设备及计算机可读存储介质
US11941863B2 (en) * 2021-08-04 2024-03-26 Datalogic Ip Tech S.R.L. Imaging system and method using a multi-layer model approach to provide robust object detection

Citations (28)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2246230A (en) 1990-06-25 1992-01-22 Gersan Ets Shape sorting
US5231677A (en) * 1984-12-28 1993-07-27 Canon Kabushiki Kaisha Image processing method and apparatus
JPH06309465A (ja) 1993-04-21 1994-11-04 Nippon Telegr & Teleph Corp <Ntt> 線図形学習認識方法
US5504318A (en) * 1991-09-13 1996-04-02 Symbol Technologies, Inc. Analog waveform decoder using peak locations
US5537494A (en) * 1990-05-29 1996-07-16 Axiom Innovation Limited Video image data encoding and compression system using edge detection and luminance profile matching
US5615324A (en) * 1994-03-17 1997-03-25 Fujitsu Limited Distributed image processing apparatus
US5642166A (en) * 1994-08-30 1997-06-24 Samsung Electronics Co., Ltd. Bi-directional motion estimation method and apparatus thereof
JPH1055447A (ja) 1996-05-21 1998-02-24 Monorisu:Kk オブジェクト認識方法およびその方法を用いた装置
US5751853A (en) * 1996-01-02 1998-05-12 Cognex Corporation Locating shapes in two-dimensional space curves
US5974175A (en) * 1997-01-22 1999-10-26 Fujitsu Limited Image processing apparatus and method for detecting a contour of an object from images of a motion picture and extracting the object therefrom
US6005978A (en) * 1996-02-07 1999-12-21 Cognex Corporation Robust search for image features across image sequences exhibiting non-uniform changes in brightness
US6014461A (en) * 1994-11-30 2000-01-11 Texas Instruments Incorporated Apparatus and method for automatic knowlege-based object identification
US6029173A (en) * 1997-11-26 2000-02-22 Navigation Technologies Corporation Method and system for representation and use of shape information in geographic databases
US6055340A (en) * 1997-02-28 2000-04-25 Fuji Photo Film Co., Ltd. Method and apparatus for processing digital images to suppress their noise and enhancing their sharpness
US6061151A (en) * 1996-02-21 2000-05-09 Canon Kabushiki Kaisha Image processing method and apparatus
US6137913A (en) * 1998-08-05 2000-10-24 Electronics And Telecommunications Research Institute Method for segmenting moving picture objects by contour tracking
GB2352076A (en) * 1999-07-15 2001-01-17 Mitsubishi Electric Inf Tech Representing and searching for an object in an image
US6256409B1 (en) * 1998-10-19 2001-07-03 Sony Corporation Method for determining a correlation between images using multi-element image descriptors
US6269358B1 (en) * 1998-04-22 2001-07-31 Nec Usa Inc Method and system for similarity-based image classification
US6307964B1 (en) * 1999-06-04 2001-10-23 Mitsubishi Electric Research Laboratories, Inc. Method for ordering image spaces to represent object shapes
US6400846B1 (en) * 1999-06-04 2002-06-04 Mitsubishi Electric Research Laboratories, Inc. Method for ordering image spaces to search for object surfaces
US6424746B1 (en) * 1997-10-28 2002-07-23 Ricoh Company, Ltd. Figure classifying method, figure classifying system, feature extracting method for figure classification, method for producing table for figure classification, information recording medium, method for evaluating degree of similarity or degree of difference between figures, figure normalizing method, and method for determining correspondence between figures
US6678413B1 (en) * 2000-11-24 2004-01-13 Yiqing Liang System and method for object identification and behavior characterization using video analysis
US6931154B1 (en) * 1999-07-05 2005-08-16 Mitsubishi Denki Kabushiki Kaisha Method and device for displaying or searching for object in image and computer-readable storage medium
US6965688B2 (en) * 2000-06-12 2005-11-15 Fujitsu Nagano Systems Engineering Limited Three-dimensional model analyzing apparatus detecting and smoothing edges before analytic operation
US20050256399A1 (en) * 2004-05-12 2005-11-17 Sirohey Saad A Methods for suppression of items and areas of interest during visualization
US20050276443A1 (en) * 2004-05-28 2005-12-15 Slamani Mohamed A Method and apparatus for recognizing an object within an image
US20080253654A1 (en) * 2005-10-11 2008-10-16 Wolf Delong Method for segmentation in an n-dimensional feature space and method for classifying objects in an n-dimensional data space which are segmented on the basis of geometric characteristics

Family Cites Families (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5579471A (en) * 1992-11-09 1996-11-26 International Business Machines Corporation Image query system and method
US5893095A (en) * 1996-03-29 1999-04-06 Virage, Inc. Similarity engine for content-based retrieval of images
US6714679B1 (en) * 1998-02-05 2004-03-30 Cognex Corporation Boundary analyzer
JP2000187731A (ja) * 1998-12-21 2000-07-04 Ricoh Co Ltd 画像特徴抽出方法およびその方法の各工程をコンピュータに実行させるためのプログラムを記録したコンピュータ読み取り可能な記録媒体
KR100671098B1 (ko) * 1999-02-01 2007-01-17 주식회사 팬택앤큐리텔 모양정보를 이용한 멀티미디어 데이터의 검색 방법 및 장치
GB2349493B (en) * 1999-04-29 2002-10-30 Mitsubishi Electric Inf Tech Method of representing an object using shape
US6941323B1 (en) * 1999-08-09 2005-09-06 Almen Laboratories, Inc. System and method for image comparison and retrieval by enhancing, defining, and parameterizing objects in images
JP3316758B2 (ja) * 1999-12-13 2002-08-19 インターナショナル・ビジネス・マシーンズ・コーポレーション モルフィング処理装置、記憶媒体、および動画作成装置
KR100370220B1 (ko) * 2000-12-27 2003-02-12 삼성전자 주식회사 직선 기반의 영상 정합 방법
US7616818B2 (en) * 2003-02-19 2009-11-10 Agfa Healthcare Method of determining the orientation of an image
EP1609092A2 (de) * 2003-04-03 2005-12-28 Dublin City University Formvergleichsverfahren zur indexierung und wiedergabe von multimediadaten
JP4355341B2 (ja) * 2003-05-29 2009-10-28 本田技研工業株式会社 深度データを用いたビジュアルトラッキング
US8745162B2 (en) * 2006-08-22 2014-06-03 Yahoo! Inc. Method and system for presenting information with multiple views

Patent Citations (29)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5231677A (en) * 1984-12-28 1993-07-27 Canon Kabushiki Kaisha Image processing method and apparatus
US5537494A (en) * 1990-05-29 1996-07-16 Axiom Innovation Limited Video image data encoding and compression system using edge detection and luminance profile matching
GB2246230A (en) 1990-06-25 1992-01-22 Gersan Ets Shape sorting
US5504318A (en) * 1991-09-13 1996-04-02 Symbol Technologies, Inc. Analog waveform decoder using peak locations
JPH06309465A (ja) 1993-04-21 1994-11-04 Nippon Telegr & Teleph Corp <Ntt> 線図形学習認識方法
US5615324A (en) * 1994-03-17 1997-03-25 Fujitsu Limited Distributed image processing apparatus
US5642166A (en) * 1994-08-30 1997-06-24 Samsung Electronics Co., Ltd. Bi-directional motion estimation method and apparatus thereof
US6014461A (en) * 1994-11-30 2000-01-11 Texas Instruments Incorporated Apparatus and method for automatic knowlege-based object identification
US5751853A (en) * 1996-01-02 1998-05-12 Cognex Corporation Locating shapes in two-dimensional space curves
US6005978A (en) * 1996-02-07 1999-12-21 Cognex Corporation Robust search for image features across image sequences exhibiting non-uniform changes in brightness
US6061151A (en) * 1996-02-21 2000-05-09 Canon Kabushiki Kaisha Image processing method and apparatus
JPH1055447A (ja) 1996-05-21 1998-02-24 Monorisu:Kk オブジェクト認識方法およびその方法を用いた装置
US5974175A (en) * 1997-01-22 1999-10-26 Fujitsu Limited Image processing apparatus and method for detecting a contour of an object from images of a motion picture and extracting the object therefrom
US6055340A (en) * 1997-02-28 2000-04-25 Fuji Photo Film Co., Ltd. Method and apparatus for processing digital images to suppress their noise and enhancing their sharpness
US6424746B1 (en) * 1997-10-28 2002-07-23 Ricoh Company, Ltd. Figure classifying method, figure classifying system, feature extracting method for figure classification, method for producing table for figure classification, information recording medium, method for evaluating degree of similarity or degree of difference between figures, figure normalizing method, and method for determining correspondence between figures
US6029173A (en) * 1997-11-26 2000-02-22 Navigation Technologies Corporation Method and system for representation and use of shape information in geographic databases
US6269358B1 (en) * 1998-04-22 2001-07-31 Nec Usa Inc Method and system for similarity-based image classification
US6137913A (en) * 1998-08-05 2000-10-24 Electronics And Telecommunications Research Institute Method for segmenting moving picture objects by contour tracking
US6256409B1 (en) * 1998-10-19 2001-07-03 Sony Corporation Method for determining a correlation between images using multi-element image descriptors
US6307964B1 (en) * 1999-06-04 2001-10-23 Mitsubishi Electric Research Laboratories, Inc. Method for ordering image spaces to represent object shapes
US6400846B1 (en) * 1999-06-04 2002-06-04 Mitsubishi Electric Research Laboratories, Inc. Method for ordering image spaces to search for object surfaces
US6931154B1 (en) * 1999-07-05 2005-08-16 Mitsubishi Denki Kabushiki Kaisha Method and device for displaying or searching for object in image and computer-readable storage medium
GB2352076A (en) * 1999-07-15 2001-01-17 Mitsubishi Electric Inf Tech Representing and searching for an object in an image
US6965688B2 (en) * 2000-06-12 2005-11-15 Fujitsu Nagano Systems Engineering Limited Three-dimensional model analyzing apparatus detecting and smoothing edges before analytic operation
US6678413B1 (en) * 2000-11-24 2004-01-13 Yiqing Liang System and method for object identification and behavior characterization using video analysis
US7068842B2 (en) * 2000-11-24 2006-06-27 Cleversys, Inc. System and method for object identification and behavior characterization using video analysis
US20050256399A1 (en) * 2004-05-12 2005-11-17 Sirohey Saad A Methods for suppression of items and areas of interest during visualization
US20050276443A1 (en) * 2004-05-28 2005-12-15 Slamani Mohamed A Method and apparatus for recognizing an object within an image
US20080253654A1 (en) * 2005-10-11 2008-10-16 Wolf Delong Method for segmentation in an n-dimensional feature space and method for classifying objects in an n-dimensional data space which are segmented on the basis of geometric characteristics

Non-Patent Citations (11)

* Cited by examiner, † Cited by third party
Title
David Yuk-Ming Chan and Irwin King, "Genetic Algorithm for Weights Assignment in Dissimilarity Funtion for Trademark Retrieval," VISUAL 1999: 557-565.
F. Mokhtarian, "Silhouette-based occluded object recognition through curvature scale space," Machine Vision and Applications (1997), pp. 87-97.
F. Mokhtarian, et al., Lecture Notes in Computer Science, vol. 1311, pp. 140-147, "A New Approach to Computation of Curvature Scale Space Image for Shape Similarity Retrievel", 1997.
Farzin Mokhtarian, Sadegh Abbasi and Josef Kittler, "Indexing and Image Database by Shape Content Using Curvature Scale Space," Proc. IEE Colloquium on Intelligent Databases, London, 1996.
Farzin Mokhtarian, Sadegh Abbasi and Josef Kittler, "Robust and Efficient Shape Indexing through Curvature Scale Space," Proc. British Machine Vision Conference, pp. 53-62, Edinburgh, UK, 1996.
G. Bebis, et al., Pattern Recognition, vol. 32, No. 7, pp. 1175-1201, "Curvature Scale-Space-Driven Object Recognition With an Indexing Scheme Based on Artificial Neutral Networks", Jul. 1999.
S. Abbasi, et al., Challenge of Image Retrieval, pp. 1-10, "Robustness of Shape Similarity Retrieval Under Affine Transformation", 1999.
S. Abbasi, et al., Lecture Notes in Computer Science, vol. 1252, pp. 284-295, "Reliable Classification of Chrysanthemum Leaves Through Curvature Scale Space", 1997.
U.S. Appl. No. 09/763,852, filed Feb. 28, 2001, pending.
U.S. Appl. No. 09/786,161, filed Mar. 2, 2001, pending.
U.S. Appl. No. 09/786,352, filed Mar. 13, 2001, pending.

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